{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# -- 读取数据并存为一个名叫apple的数据框\n",
    "# -- 查看每一列的数据类型\n",
    "# -- 将Date这个列转换为datetime类型\n",
    "# -- 将Date设置为索引\n",
    "# -- 有重复的日期吗？\n",
    "# -- 将index设置为升序\n",
    "# -- 找到每个月的最后一个交易日(business day)\n",
    "# -- 数据集中最早的日期和最晚的日期相差多少天？\n",
    "# -- 在数据中一共有多少个月？\n",
    "# -- 按照时间顺序可视化Adj Close值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "#读取数据并存为一个名叫apple的数据框\n",
    "apple = pd.read_csv('data/appl_1980_2014.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date          object\n",
       "Open         float64\n",
       "High         float64\n",
       "Low          float64\n",
       "Close        float64\n",
       "Volume         int64\n",
       "Adj Close    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看每一列的数据类型\n",
    "apple.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-07-08</td>\n",
       "      <td>96.27</td>\n",
       "      <td>96.80</td>\n",
       "      <td>93.92</td>\n",
       "      <td>95.35</td>\n",
       "      <td>65130000</td>\n",
       "      <td>95.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-07-07</td>\n",
       "      <td>94.14</td>\n",
       "      <td>95.99</td>\n",
       "      <td>94.10</td>\n",
       "      <td>95.97</td>\n",
       "      <td>56305400</td>\n",
       "      <td>95.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-07-03</td>\n",
       "      <td>93.67</td>\n",
       "      <td>94.10</td>\n",
       "      <td>93.20</td>\n",
       "      <td>94.03</td>\n",
       "      <td>22891800</td>\n",
       "      <td>94.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-07-02</td>\n",
       "      <td>93.87</td>\n",
       "      <td>94.06</td>\n",
       "      <td>93.09</td>\n",
       "      <td>93.48</td>\n",
       "      <td>28420900</td>\n",
       "      <td>93.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-07-01</td>\n",
       "      <td>93.52</td>\n",
       "      <td>94.07</td>\n",
       "      <td>93.13</td>\n",
       "      <td>93.52</td>\n",
       "      <td>38170200</td>\n",
       "      <td>93.52</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date   Open   High    Low  Close    Volume  Adj Close\n",
       "0  2014-07-08  96.27  96.80  93.92  95.35  65130000      95.35\n",
       "1  2014-07-07  94.14  95.99  94.10  95.97  56305400      95.97\n",
       "2  2014-07-03  93.67  94.10  93.20  94.03  22891800      94.03\n",
       "3  2014-07-02  93.87  94.06  93.09  93.48  28420900      93.48\n",
       "4  2014-07-01  93.52  94.07  93.13  93.52  38170200      93.52"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "apple.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-07-08</td>\n",
       "      <td>96.27</td>\n",
       "      <td>96.80</td>\n",
       "      <td>93.92</td>\n",
       "      <td>95.35</td>\n",
       "      <td>65130000</td>\n",
       "      <td>95.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-07-07</td>\n",
       "      <td>94.14</td>\n",
       "      <td>95.99</td>\n",
       "      <td>94.10</td>\n",
       "      <td>95.97</td>\n",
       "      <td>56305400</td>\n",
       "      <td>95.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-07-03</td>\n",
       "      <td>93.67</td>\n",
       "      <td>94.10</td>\n",
       "      <td>93.20</td>\n",
       "      <td>94.03</td>\n",
       "      <td>22891800</td>\n",
       "      <td>94.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-07-02</td>\n",
       "      <td>93.87</td>\n",
       "      <td>94.06</td>\n",
       "      <td>93.09</td>\n",
       "      <td>93.48</td>\n",
       "      <td>28420900</td>\n",
       "      <td>93.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-07-01</td>\n",
       "      <td>93.52</td>\n",
       "      <td>94.07</td>\n",
       "      <td>93.13</td>\n",
       "      <td>93.52</td>\n",
       "      <td>38170200</td>\n",
       "      <td>93.52</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date   Open   High    Low  Close    Volume  Adj Close\n",
       "0 2014-07-08  96.27  96.80  93.92  95.35  65130000      95.35\n",
       "1 2014-07-07  94.14  95.99  94.10  95.97  56305400      95.97\n",
       "2 2014-07-03  93.67  94.10  93.20  94.03  22891800      94.03\n",
       "3 2014-07-02  93.87  94.06  93.09  93.48  28420900      93.48\n",
       "4 2014-07-01  93.52  94.07  93.13  93.52  38170200      93.52"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "apple['Date'] = pd.to_datetime(apple['Date'])\n",
    "apple.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-07-08</th>\n",
       "      <td>96.27</td>\n",
       "      <td>96.80</td>\n",
       "      <td>93.92</td>\n",
       "      <td>95.35</td>\n",
       "      <td>65130000</td>\n",
       "      <td>95.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-07-07</th>\n",
       "      <td>94.14</td>\n",
       "      <td>95.99</td>\n",
       "      <td>94.10</td>\n",
       "      <td>95.97</td>\n",
       "      <td>56305400</td>\n",
       "      <td>95.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-07-03</th>\n",
       "      <td>93.67</td>\n",
       "      <td>94.10</td>\n",
       "      <td>93.20</td>\n",
       "      <td>94.03</td>\n",
       "      <td>22891800</td>\n",
       "      <td>94.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-07-02</th>\n",
       "      <td>93.87</td>\n",
       "      <td>94.06</td>\n",
       "      <td>93.09</td>\n",
       "      <td>93.48</td>\n",
       "      <td>28420900</td>\n",
       "      <td>93.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-07-01</th>\n",
       "      <td>93.52</td>\n",
       "      <td>94.07</td>\n",
       "      <td>93.13</td>\n",
       "      <td>93.52</td>\n",
       "      <td>38170200</td>\n",
       "      <td>93.52</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Open   High    Low  Close    Volume  Adj Close\n",
       "Date                                                       \n",
       "2014-07-08  96.27  96.80  93.92  95.35  65130000      95.35\n",
       "2014-07-07  94.14  95.99  94.10  95.97  56305400      95.97\n",
       "2014-07-03  93.67  94.10  93.20  94.03  22891800      94.03\n",
       "2014-07-02  93.87  94.06  93.09  93.48  28420900      93.48\n",
       "2014-07-01  93.52  94.07  93.13  93.52  38170200      93.52"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#将Date设置为索引\n",
    "apple = apple.set_index('Date')\n",
    "apple.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#有重复的日期吗？\n",
    "apple.index.is_unique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1980-12-12</th>\n",
       "      <td>28.75</td>\n",
       "      <td>28.87</td>\n",
       "      <td>28.75</td>\n",
       "      <td>28.75</td>\n",
       "      <td>117258400</td>\n",
       "      <td>0.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-12-15</th>\n",
       "      <td>27.38</td>\n",
       "      <td>27.38</td>\n",
       "      <td>27.25</td>\n",
       "      <td>27.25</td>\n",
       "      <td>43971200</td>\n",
       "      <td>0.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-12-16</th>\n",
       "      <td>25.37</td>\n",
       "      <td>25.37</td>\n",
       "      <td>25.25</td>\n",
       "      <td>25.25</td>\n",
       "      <td>26432000</td>\n",
       "      <td>0.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-12-17</th>\n",
       "      <td>25.87</td>\n",
       "      <td>26.00</td>\n",
       "      <td>25.87</td>\n",
       "      <td>25.87</td>\n",
       "      <td>21610400</td>\n",
       "      <td>0.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-12-18</th>\n",
       "      <td>26.63</td>\n",
       "      <td>26.75</td>\n",
       "      <td>26.63</td>\n",
       "      <td>26.63</td>\n",
       "      <td>18362400</td>\n",
       "      <td>0.41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Open   High    Low  Close     Volume  Adj Close\n",
       "Date                                                        \n",
       "1980-12-12  28.75  28.87  28.75  28.75  117258400       0.45\n",
       "1980-12-15  27.38  27.38  27.25  27.25   43971200       0.42\n",
       "1980-12-16  25.37  25.37  25.25  25.25   26432000       0.39\n",
       "1980-12-17  25.87  26.00  25.87  25.87   21610400       0.40\n",
       "1980-12-18  26.63  26.75  26.63  26.63   18362400       0.41"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#将index设置为升序\n",
    "apple = apple.sort_index(ascending=True)\n",
    "apple.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1980-12-31</th>\n",
       "      <td>30.481538</td>\n",
       "      <td>30.567692</td>\n",
       "      <td>30.443077</td>\n",
       "      <td>30.443077</td>\n",
       "      <td>2.586252e+07</td>\n",
       "      <td>0.473077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-30</th>\n",
       "      <td>31.754762</td>\n",
       "      <td>31.826667</td>\n",
       "      <td>31.654762</td>\n",
       "      <td>31.654762</td>\n",
       "      <td>7.249867e+06</td>\n",
       "      <td>0.493810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-02-27</th>\n",
       "      <td>26.480000</td>\n",
       "      <td>26.572105</td>\n",
       "      <td>26.407895</td>\n",
       "      <td>26.407895</td>\n",
       "      <td>4.231832e+06</td>\n",
       "      <td>0.411053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-03-31</th>\n",
       "      <td>24.937727</td>\n",
       "      <td>25.016818</td>\n",
       "      <td>24.836364</td>\n",
       "      <td>24.836364</td>\n",
       "      <td>7.962691e+06</td>\n",
       "      <td>0.387727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-04-30</th>\n",
       "      <td>27.286667</td>\n",
       "      <td>27.368095</td>\n",
       "      <td>27.227143</td>\n",
       "      <td>27.227143</td>\n",
       "      <td>6.392000e+06</td>\n",
       "      <td>0.423333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Open       High        Low      Close        Volume  \\\n",
       "Date                                                                   \n",
       "1980-12-31  30.481538  30.567692  30.443077  30.443077  2.586252e+07   \n",
       "1981-01-30  31.754762  31.826667  31.654762  31.654762  7.249867e+06   \n",
       "1981-02-27  26.480000  26.572105  26.407895  26.407895  4.231832e+06   \n",
       "1981-03-31  24.937727  25.016818  24.836364  24.836364  7.962691e+06   \n",
       "1981-04-30  27.286667  27.368095  27.227143  27.227143  6.392000e+06   \n",
       "\n",
       "            Adj Close  \n",
       "Date                   \n",
       "1980-12-31   0.473077  \n",
       "1981-01-30   0.493810  \n",
       "1981-02-27   0.411053  \n",
       "1981-03-31   0.387727  \n",
       "1981-04-30   0.423333  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#找到每个月的最后一个交易日(business day)\n",
    "#resample参数详解：https://www.jianshu.com/p/5367ef7453ce\n",
    "apple_month = apple.resample('BME').mean()\n",
    "apple_month.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12261"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#数据集中最早的日期和最晚的日期相差多少天？\n",
    "(apple.index.max() - apple.index.min()).days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "404"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(apple_month)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 0.64\n",
      "RMSE: 0.94\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/ml/lib/python3.10/site-packages/sklearn/utils/validation.py:2749: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/envs/ml/lib/python3.10/site-packages/sklearn/utils/validation.py:2749: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/envs/ml/lib/python3.10/site-packages/sklearn/utils/validation.py:2749: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/envs/ml/lib/python3.10/site-packages/sklearn/utils/validation.py:2749: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/envs/ml/lib/python3.10/site-packages/sklearn/utils/validation.py:2749: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Predicted Adj Close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-07-09</th>\n",
       "      <td>96.216131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-07-10</th>\n",
       "      <td>96.408054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-07-11</th>\n",
       "      <td>96.657592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-07-14</th>\n",
       "      <td>96.972401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-07-15</th>\n",
       "      <td>97.249000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Predicted Adj Close\n",
       "2014-07-09            96.216131\n",
       "2014-07-10            96.408054\n",
       "2014-07-11            96.657592\n",
       "2014-07-14            96.972401\n",
       "2014-07-15            97.249000"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_absolute_error, mean_squared_error\n",
    "\n",
    "adj_close = apple[['Adj Close']].copy()\n",
    "lag_features = 5\n",
    "for lag in range(1, lag_features + 1):\n",
    "    adj_close[f'lag_{lag}'] = adj_close['Adj Close'].shift(lag)\n",
    "\n",
    "feature_df = adj_close.dropna()\n",
    "X = feature_df[[f'lag_{lag}' for lag in range(1, lag_features + 1)]]\n",
    "y = feature_df['Adj Close']\n",
    "\n",
    "split_idx = int(len(feature_df) * 0.8)\n",
    "X_train, X_test = X.iloc[:split_idx], X.iloc[split_idx:]\n",
    "y_train, y_test = y.iloc[:split_idx], y.iloc[split_idx:]\n",
    "\n",
    "model = LinearRegression()\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "y_pred = model.predict(X_test)\n",
    "mae = mean_absolute_error(y_test, y_pred)\n",
    "rmse = np.sqrt(mean_squared_error(y_test, y_pred))\n",
    "print(f'MAE: {mae:.2f}')\n",
    "print(f'RMSE: {rmse:.2f}')\n",
    "\n",
    "plt.figure(figsize=(12, 4))\n",
    "plt.plot(y_test.index, y_test, label='Actual', linewidth=2)\n",
    "plt.plot(y_test.index, y_pred, label='Predicted', linewidth=2)\n",
    "plt.title('Apple Adj Close Prediction (Test Set)')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Adj Close')\n",
    "plt.legend()\n",
    "plt.xticks(rotation=45)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# Use the last available (non-NaN) lag feature row from X as the starting lags for forecasting\n",
    "# This is safer than slicing raw `apple['Adj Close']` which may contain NaNs at the tail\n",
    "current_lags = X.iloc[-1].tolist()\n",
    "future_steps = 5\n",
    "future_dates = pd.date_range(start=apple.index[-1] + pd.offsets.BDay(1), periods=future_steps, freq='B')\n",
    "future_preds = []\n",
    "\n",
    "for _ in range(future_steps):\n",
    "    next_price = model.predict([current_lags])[0]\n",
    "    future_preds.append(next_price)\n",
    "    current_lags = [next_price] + current_lags[:-1]\n",
    "\n",
    "future_forecast = pd.DataFrame({'Predicted Adj Close': future_preds}, index=future_dates)\n",
    "future_forecast\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "ml",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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